public CarSnapshotGraph createCarSnapshotGraph(Bitmap bi) {
CarSnapshotGraph graph = new CarSnapshotGraph(this);
float[] peaks = new float[bi.getHeight()];
/** Graph at vertical position */
NativeGraphics.getHSVBrightness(bi, peaks);
graph.addPeaks(peaks);
return graph;
}
public Bitmap verticalEdgeBi(Bitmap source) {
int template[] = {
-1, 0, 1,
-1, 0, 1,
-1, 0, 1
};
return NativeGraphics.convolve(source, template, 3, 3, 1, 0);
}
private ArrayList<Graph.Peak> computeGraph(Bitmap img) {
Bitmap dest = verticalEdgeBi(img);
dest = NativeGraphics.treshold(dest, 150);
// Intelligence.console.consoleBitmap(dest);
CarSnapshotGraph graphHandle = this.createCarSnapshotGraph(dest);
graphHandle.rankFilter(carsnapshot_graphrankfilter);
graphHandle.applyProbabilityDistributor(distributor);
/** .40 - min .45 - ideal .50 - max */
graphHandle.findPeaks(2, 2, .40f); // We find two candidate
// Intelligence.console.consoleBitmap(graphHandle.renderVertically(50, 300));
dest.recycle();
return graphHandle.peaks;
}
public ArrayList<Band> getBands() {
ArrayList<Band> out = new ArrayList<Band>();
CopyOnWriteArrayList<Challenger> out2 = new CopyOnWriteArrayList<Challenger>();
int imageWidth = this.image.getWidth();
/** ideal - 25 minimum - 20 maximum - 30 */
int step = 25;
/** ideal - 4 minimum - 3 maximum - 5 */
int countPlates = 3;
float stickyCoef = 0.2f; // Value in percents, show coincidence between two challenger-images
int imageWidthIteration = imageWidth / step;
int imageLength = imageWidthIteration * step;
// Bitmap dest = NativeGraphics.convert565to8888(image); //Preprocessing for source image
Bitmap dest = verticalEdgeBi(image);
// Intelligence.console.consoleBitmap(image);
dest = NativeGraphics.treshold(dest, 80);
/** Render processing - console */
// ConsoleGraph cGraph = Intelligence.console.createConsoleGraph(dest, step);
for (int i = 0; i < imageLength - step; i += step) {
Bitmap bi = Bitmap.createBitmap(dest, i, 0, step, dest.getHeight());
ChallengerGraph graphHandle = this.createChallengerGraph(bi);
graphHandle.rankFilter(carsnapshot_graphrankfilter);
graphHandle.applyProbabilityDistributor(distributor);
for (Peak p : graphHandle.findPeaks(numberOfCandidates, 6, .55f)) {
// cGraph.drawLine(i, p.center);
boolean isValidPeak = false;
for (Challenger elm : out2) {
if (elm.addPeak(p, i)) {
isValidPeak = true;
} else if ((elm.getStep() < (i - step)) && elm.elems.size() < countPlates) {
out2.remove(elm);
}
}
if (!isValidPeak) {
Challenger chlgr = new Challenger(p, i, step);
out2.add(chlgr);
}
}
}
/** Join equal images */
LinkedList<Challenger> out3 = new LinkedList<Challenger>();
for (Challenger elm : out2) {
float elmSizeX = elm.maxX - elm.minX;
float elmSizeY = elm.maxY - elm.minY;
if (elm.elems.size() < countPlates) continue;
if ((elm.maxX <= elm.minX) || (elm.maxY <= elm.minY)) continue;
if (elmSizeX / elmSizeY < 1) continue;
boolean isOk = false;
for (Challenger elm2 : out3) {
float elm2SizeX = elm2.maxX - elm2.minX;
float elm2SizeY = elm2.maxY - elm2.minY;
float diffX = 0;
float diffY = 0;
if (elm2.maxY > elm.maxY) {
diffY = elm.maxY - elm2.minY;
} else {
diffY = elm2.maxY - elm.minY;
}
if (elm2.maxX > elm.maxX) {
diffX = elm.maxX - elm2.minX;
} else {
diffX = elm2.maxX - elm.minX;
}
if (diffY > 0
&& diffX > 0
&& (((diffY / elm2SizeY) > stickyCoef) || ((diffY / elmSizeY) > stickyCoef))
&& (((diffX / elm2SizeX) > stickyCoef) || ((diffX / elmSizeX) > stickyCoef))) {
elm2.maxX = Math.max(elm.maxX, elm2.maxX);
elm2.minX = Math.min(elm.minX, elm2.minX);
elm2.maxY = Math.max(elm.maxY, elm2.maxY);
elm2.minY = Math.min(elm.minY, elm2.minY);
isOk = true;
}
}
if (isOk == false) {
out3.add(elm);
}
}
int amplify = 3;
/**
* We find original picture with original dimensions and then we project work image to original
* picture
*/
for (Challenger elm : out3) {
Bitmap bi = null;
int x = 0, y = 0, w = 0, h = 0;
float power = 1.04f;
if (originalImage != null) {
float coefWidth = (float) originalImage.getWidth() / (float) dest.getWidth();
float coefHeight = (float) originalImage.getHeight() / (float) dest.getHeight();
x = (int) (Math.max(0, elm.minX - amplify) * coefWidth);
y = (int) (Math.max(0, elm.minY - amplify) * coefHeight);
w = (int) (Math.max(1, elm.maxX - elm.minX + amplify) * coefWidth * power);
h = (int) (Math.max(1, elm.maxY - elm.minY + amplify) * coefHeight * power);
} else {
originalImage = image;
x = Math.max(0, elm.minX - amplify);
y = Math.max(0, elm.minY - amplify);
w = (int) (Math.max(1, elm.maxX - elm.minX + amplify) * power);
h = (int) (Math.max(1, elm.maxY - elm.minY + amplify) * power);
}
if (x + w >= originalImage.getWidth()) continue;
if (y + h >= originalImage.getHeight()) continue;
bi = Bitmap.createBitmap(originalImage, x, y, w, h);
for (Graph.Peak p : computeGraph(bi)) {
Bitmap bi2 = Bitmap.createBitmap(bi, 0, p.getLeft(), bi.getWidth(), p.getDiff());
out.add(new Band(bi2));
}
}
out2.clear();
out3.clear();
Intelligence.console.clear();
return out;
}
